Predictive Science - AI Engineering & Prompt Architecture Lead - Vice President at JPMC Candidate Experience page — Hyderabad, IND | Base Career | Base Career
Lead and develop a team of 8–15 prompt engineers and AI/ML developers; set technical direction, run architecture reviews, and define quality standards for prompts and model artifacts.
Architect agentic, multi-step AI workflows chaining classification → extraction → cross-validation → exception routing with human-in-the-loop checkpoints.
Debug and remediate complex prompt failures (context-window overflow, instruction drift in long chains, RAG retrieval poisoning, output/format instability).
Design prompt/model evaluation frameworks measuring accuracy plus consistency, robustness, latency, cost-per-call, and hallucination rate.
Operationalize prompt lifecycle management as production code: versioning, CI/CD prompt tests, A/B experiments, rollback, and audited change history.
Guide model selection and optimization (prompting vs fine-tuning vs custom training) balancing accuracy, latency, cost, and data sensitivity.
Design RAG architectures for financial documents: chunking, embeddings, vector store design, re-ranking, and context injection.
Oversee fine-tuning/training workflows: dataset curation, annotation quality, training configurations, and generalization across document variants.
Build and maintain evaluation infrastructure: benchmark/golden datasets, regression suites, and automated scoring to catch regressions pre-production.
Define confidence calibration and escalation logic so systems estimate uncertainty and route low-confidence outputs to human reviewers with the right context.
Partner with governance/model risk and data engineering: produce validator-ready documentation (explainability/auditability) and ensure robust, refreshed data/annotation/eval pipelines; drive AI-native development practices to improve velocity.
Required qualifications, capabilities and skills
10+ years in NLP/AI/ML or computational linguistics, including 3+ years leading technical teams with direct reports.
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Strong RAG system design experience: embeddings trade-offs (e.g., ada/BGE/Cohere), chunking for semi-structured docs, hybrid retrieval (dense+sparse), and re-ranking.
Fine-tuning experience: LoRA/QLoRA, instruction tuning, RLHF/DPO, dataset curation, and evaluating tuned vs prompted performance.
Python + ML engineering proficiency: PyTorch, Hugging Face, LangChain/LlamaIndex (or equivalents), vector DBs (Pinecone/Weaviate/pgvector), and API development.
AI evaluation systems experience beyond F1: faithfulness, answer relevance, RAG context precision/recall, automated eval pipelines, and LLM-as-judge.
Deep understanding of LLM failure modes: hallucinations, sycophancy, long-context instruction degradation, prompt-format sensitivity, and catastrophic forgetting.
Structured output enforcement in production: JSON mode, function calling, constrained decoding, output parsers, and schema validation.
Build-vs-buy/model selection track record: benchmarking foundation models (GPT-4/Claude/Llama/Mistral) against task requirements.
Leadership under ambiguity: pragmatic trade-offs, rapid iteration as practices evolve, and strong communication with compliance, risk, and business stakeholders.
Preferred qualifications, capabilities and skills
Domain expertise in KYC/AML or financial document processing: entity extraction from registries, beneficial ownership structures, sanctions screening logic, adverse media classification.
Experience designing autonomous AI agents: tool-use patterns, planning/reasoning loops, memory architectures, and safety guardrails for regulated environments.
Knowledge of AI security/adversarial robustness: prompt injection defense, jailbreak detection, data poisoning awareness, and output monitoring for sensitive financial data.
Experience with model distillation to produce smaller, faster models for cost-effective deployment.
Familiarity with AI observability/monitoring: tracking prompt/model performance, drift detection, alerting, and health dashboards.
Experience with multi-modal AI combining OCR, layout understanding, and LLM-based extraction for complex documents.
Advanced degree (MS/PhD) in CS/NLP/ML or equivalent depth via publications, open-source, or production system design and Passion for talent development: growing engineers from junior prompt writers into senior AI system designers via structured mentorship.
Company
JPMorganChase, one of the oldest financial institutions, offers innovative financial solutions to millions of consumers, small businesses and many of the world’s most prominent corporate, institutional and government clients under the J.P. Morgan and Chase brands. Our history spans over 200 years and today we are a leader in investment banking, consumer and small business banking, commercial banking, financial transaction processing and asset management. We recognize that our people are our strength and the diverse talents they bring to our global workforce are directly linked to our success. We are an equal opportunity employer and place a high value on diversity and inclusion at our company. We do not discriminate on the basis of any protected attribute, including race, religion, color, national origin, gender, sexual orientation, gender identity, gender expression, age, marital or veteran status, pregnancy or disability, or any other basis protected under applicable law. We also make reasonable accommodations for applicants’ and employees’ religious practices and beliefs, as well as mental health or physical disability needs. Visit our FAQs for more information about requesting an accommodation.
About JPMC Candidate Experience page
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